11 citations · 18 across the 11 of their papers we have counts for
11 papers
ReXInTheWild: A Unified Benchmark for Medical Photograph Understanding
Oishi Banerjee, Sung Eun Kim, Alexandra N. Willauer +9
Everyday photographs taken with ordinary cameras are already widely used in telemedicine and other online health conversations, yet no comprehensive benchmark evaluates whether vis…
Voice-guided Orchestrated Intelligence for Clinical Evaluation (VOICE): A Voice AI Agent System for Prehospital Stroke Assessment
Julian Acosta, Scott Adams, Julius Kernbach +7
We developed a voice-driven artificial intelligence (AI) system that guides anyone - from paramedics to family members - through expert-level stroke evaluations using natural conve…
ReXGradient-160K: A Large-Scale Publicly Available Dataset of Chest Radiographs with Free-text Reports
Xiaoman Zhang, Julián N. Acosta, Josh Miller +2
We present ReXGradient-160K, representing the largest publicly available chest X-ray dataset to date in terms of the number of patients. This dataset contains 160,000 chest X-ray s…
a2z-1 for Multi-Disease Detection in Abdomen-Pelvis CT: External Validation and Performance Analysis Across 21 Conditions
Pranav Rajpurkar, Julian N. Acosta, Siddhant Dogra +4
We present a comprehensive evaluation of a2z-1, an artificial intelligence (AI) model designed to analyze abdomen-pelvis CT scans for 21 time-sensitive and actionable findings. Our…
The Impact of AI Assistance on Radiology Reporting: A Pilot Study Using Simulated AI Draft Reports
Julián N. Acosta, Siddhant Dogra, Subathra Adithan +4
Radiologists face increasing workload pressures amid growing imaging volumes, creating risks of burnout and delayed reporting times. While artificial intelligence (AI) based automa…
RadFlag: A Black-Box Hallucination Detection Method for Medical Vision Language Models
Serena Zhang, Sraavya Sambara, Oishi Banerjee +3
Generating accurate radiology reports from medical images is a clinically important but challenging task. While current Vision Language Models (VLMs) show promise, they are prone t…
ReXrank: A Public Leaderboard for AI-Powered Radiology Report Generation
Xiaoman Zhang, Hong-Yu Zhou, Xiaoli Yang +6
AI-driven models have demonstrated significant potential in automating radiology report generation for chest X-rays. However, there is no standardized benchmark for objectively eva…
HeadCT-ONE: Enabling Granular and Controllable Automated Evaluation of Head CT Radiology Report Generation
Julián N. Acosta, Xiaoman Zhang, Siddhant Dogra +5
We present Head CT Ontology Normalized Evaluation (HeadCT-ONE), a metric for evaluating head CT report generation through ontology-normalized entity and relation extraction. HeadCT…
ReXErr: Synthesizing Clinically Meaningful Errors in Diagnostic Radiology Reports
Vishwanatha M. Rao, Serena Zhang, Julian N. Acosta +2
Accurately interpreting medical images and writing radiology reports is a critical but challenging task in healthcare. Both human-written and AI-generated reports can contain error…
Uncovering Knowledge Gaps in Radiology Report Generation Models through Knowledge Graphs
Xiaoman Zhang, Julián N. Acosta, Hong-Yu Zhou +1
Recent advancements in artificial intelligence have significantly improved the automatic generation of radiology reports. However, existing evaluation methods fail to reveal the mo…
MedVersa: A Generalist Foundation Model for Medical Image Interpretation
Hong-Yu Zhou, Julián Nicolás Acosta, Subathra Adithan +3
Current medical AI systems are often limited to narrow applications, hindering widespread adoption. We present MedVersa, a generalist foundation model trained on tens of millions o…